We present our submission to the ICASSP-SPGC-2023 ADReSS-M Challenge Task,
which aims to investigate which acoustic features can be generalized and
transferred across languages for Alzheimer's Disease (AD) prediction. The
challenge consists of two tasks: one is to classify the speech of AD patients
and healthy individuals, and the other is to infer Mini Mental State
Examination (MMSE) score based on speech only. The difficulty is mainly
embodied in the mismatch of the dataset, in which the training set is in
English while the test set is in Greek. We extract paralinguistic features
using openSmile toolkit and acoustic features using XLSR-53. In addition, we
extract linguistic features after transcribing the speech into text. These
features are used as indicators for AD detection in our method. Our method
achieves an accuracy of 69.6% on the classification task and a root mean
squared error (RMSE) of 4.788 on the regression task. The results show that our
proposed method is expected to achieve automatic multilingual Alzheimer's
Disease detection through spontaneous speech.

该研究利用 openSmile 工具包和 XLSR-53 提取声学特征，将语音转录成文本后提取语言特征进行 AD 患者检测，结果显示该方法能够通过自发性说话实现自动多语言阿尔茨海默病检测，分类准确率为 69.6%，均方根误差为 4.788。